Tag Archives: Data Integration
Last week was Informatica’s first ever Data Mania event, held at the Contemporary Jewish Museum in San Francisco. We had an A-list lineup of speakers from leading cloud and data companies, such as Salesforce, Amazon Web Services (AWS), Tableau, Dun & Bradstreet, Marketo, AppDynamics, Birst, Adobe, and Qlik. The event and speakers covered a range of topics all related to data, including Big Data processing in the cloud, data-driven customer success, and cloud analytics.
While these companies are giants today in the world of cloud and have created their own unique ecosystems, we also wanted to take a peek at and hear from the leaders of tomorrow. Before startups can become market leaders in their own realm, they face the challenge of ramping up a stellar roster of customers so that they can get to subsequent rounds of venture funding. But what gets in their way are the numerous data integration challenges of onboarding customer data onto their software platform. When these challenges remain unaddressed, R&D resources are spent on professional services instead of building value-differentiating IP. Bugs also continue to mount, and technical debt increases.
Enter the Informatica Cloud Connector SDK. Built entirely in Java and able to browse through any cloud application’s API, the Cloud Connector SDK parses the metadata behind each data object and presents it in the context of what a business user should see. We had four startups build a native connector to their application in less than two weeks: BigML, Databricks, FollowAnalytics, and ThoughtSpot. Let’s take a look at each one of them.
With predictive analytics becoming a growing imperative, machine-learning algorithms that can have a higher probability of prediction are also becoming increasingly important. BigML provides an intuitive yet powerful machine-learning platform for actionable and consumable predictive analytics. Watch their demo on how they used Informatica Cloud’s Connector SDK to help them better predict customer churn.
Can’t play the video? Click here, http://youtu.be/lop7m9IH2aw
Databricks was founded out of the UC Berkeley AMPLab by the creators of Apache Spark. Databricks Cloud is a hosted end-to-end data platform powered by Spark. It enables organizations to unlock the value of their data, seamlessly transitioning from data ingest through exploration and production. Watch their demo that showcases how the Informatica Cloud connector for Databricks Cloud was used to analyze lead contact rates in Salesforce, and also performing machine learning on a dataset built using either Scala or Python.
Can’t play the video? Click here, http://youtu.be/607ugvhzVnY
With mobile usage growing by leaps and bounds, the area of customer engagement on a mobile app has become a fertile area for marketers. Marketers are charged with acquiring new customers, increasing customer loyalty and driving new revenue streams. But without the technological infrastructure to back them up, their efforts are in vain. FollowAnalytics is a mobile analytics and marketing automation platform for the enterprise that helps companies better understand audience engagement on their mobile apps. Watch this demo where FollowAnalytics first builds a completely native connector to its mobile analytics platform using the Informatica Cloud Connector SDK and then connects it to Microsoft Dynamics CRM Online using Informatica Cloud’s prebuilt connector for it. Then, see FollowAnalytics go one step further by performing even deeper analytics on their engagement data using Informatica Cloud’s prebuilt connector for Salesforce Wave Analytics Cloud.
Can’t play the video? Click here, http://youtu.be/E568vxZ2LAg
Analytics has taken center stage this year due to the rise in cloud applications, but most of the existing BI tools out there still stick to the old way of doing BI. ThoughtSpot brings a consumer-like simplicity to the world of BI by allowing users to search for the information they’re looking for just as if they were using a search engine like Google. Watch this demo where ThoughtSpot uses Informatica Cloud’s vast library of over 100 native connectors to move data into the ThoughtSpot appliance.
Can’t play the video? Click here, http://youtu.be/6gJD6hRD9h4
As reported by the Economic Times, “In the coming years, enormous volumes of machine-generated data from the Internet of Things (IoT) will emerge. If exploited properly, this data – often dubbed machine or sensor data, and often seen as the next evolution in Big Data – can fuel a wide range of data-driven business process improvements across numerous industries.”
We can all see this happening in our personal lives. Our thermostats are connected now, our cars have been for years, even my toothbrush has a Bluetooth connection with my phone. On the industrial sides, devices have also been connected for years, tossing off megabytes of data per day that have been typically used for monitoring, with the data tossed away as quickly as it appears.
So, what changed? With the advent of big data, cheap cloud, and on-premise storage, we now have the ability to store machine or sensor data spinning out of industrial machines, airliners, health diagnostic devices, etc., and leverage that data for new and valuable uses.
For example, the ability determine the likelihood that a jet engine will fail, based upon the sensor data gathered, and how that data compared with existing known patterns of failure. Instead of getting an engine failure light on the flight deck, the pilots can see that the engine has a 20 percent likelihood of failure, and get the engine serviced before it fails completely.
The problem with all of this very cool stuff is that we need to once again rethink data integration. Indeed, if the data can’t get from the machine sensors to a persistent data store for analysis, then none of this has a chance of working.
That’s why those who are moving to IoT-based systems need to do two things. First, they must create a strategy for extracting data from devices, such as industrial robots or ann Audi A8. Second, they need a strategy to take all of this disparate data that’s firing out of devices at megabytes per second, and put it where it needs to go, and in the right native structure (or in an unstructured data lake), so it can be leveraged in useful ways, and in real time.
The challenge is that machines and devices are not traditional IT systems. I’ve built connectors for industrial applications in my career. The fact is, you need to adapt to the way that the machines and devices produce data, and not the other way around. Data integration technology needs to adapt as well, making sure that it can deal with streaming and unstructured data, including many instances where the data needs to be processed in flight as it moves from the device, to the database.
This becomes a huge opportunity for data integration providers who understand the special needs of IoT, as well as the technology that those who build IoT-based systems can leverage. However, the larger value is for those businesses that learn how to leverage IoT to provide better services to their customers by offering insights that have previously been impossible. Be it jet engine reliability, the fuel efficiency of my car, or feedback to my physician from sensors on my body, this is game changing stuff. At the heart of its ability to succeed is the ability to move data from place-to-place.
Original article can be found here, scmagazine.com
On Jan. 13 the White House announced President Barack Obama’s proposal for new data privacy legislation, the Personal Data Notification and Protection Act. Many states have laws today that require corporations and government agencies to notify consumers in the event of a breach – but it is not enough. This new proposal aims to improve cybersecurity standards nationwide with the following tactics:
Enable cyber-security information sharing between private and public sectors.
Government agencies and corporations with a vested interest in protecting our information assets need a streamlined way to communicate and share threat information. This component of the proposed legislation incents organizations that participate in knowledge-sharing with targeted liability protection, as long as they are responsible for how they share, manage and retain privacy data.
Modernize the tools law enforcement has to combat cybercrime.
Existing laws, such as the Computer Fraud and Abuse Act, need to be updated to incorporate the latest cyber-crime classifications while giving prosecutors the ability to target insiders with privileged access to sensitive and privacy data. The proposal also specifically calls out pursuing prosecution when selling privacy data nationally and internationally.
Standardize breach notification policies nationwide.
Many states have some sort of policy that requires notification of customers that their data has been compromised. Three leading examples include California , Florida’s Information Protection Act (FIPA) and Massachusetts Standards for the Protection of Personal Information of Residents of the Commonwealth. New Mexico, Alabama and South Dakota have no data breach protection legislation. Enforcing standardization and simplifying the requirement for companies to notify customers and employees when a breach occurs will ensure consistent protection no matter where you live or transact.
Invest in increasing cyber-security skill sets.
For a number of years, security professionals have reported an ever-increasing skills gap in the cybersecurity profession. In fact, in a recent Ponemon Institute report, 57 percent of respondents said a data breach incident could have been avoided if the organization had more skilled personnel with data security responsibilities. Increasingly, colleges and universities are adding cybersecurity curriculum and degrees to meet the demand. In support of this need, the proposed legislation mentions that the Department of Energy will provide $25 million in educational grants to Historically Black Colleges and Universities (HBCU) and two national labs to support a cybersecurity education consortium.
This proposal is clearly comprehensive, but it also raises the critical question: How can organizations prepare themselves for this privacy legislation?
The International Association of Privacy Professionals conducted a study of Federal Trade Commission (FTC) enforcement actions. From the report, organizations can infer best practices implied by FTC enforcement and ensure these are covered by their organization’s security architecture, policies and practices:
- Perform assessments to identify reasonably foreseeable risks to the security, integrity, and confidentiality of personal information collected and stored on the network, online or in paper files.
- Limited access policies curb unnecessary security risks and minimize the number and type of network access points that an information security team must monitor for potential violations.
- Limit employee access to (and copying of) personal information, based on employee’s role.
- Implement and monitor compliance with policies and procedures for rendering information unreadable or otherwise secure in the course of disposal. Securely disposed information must not practicably be read or reconstructed.
- Restrict third party access to personal information based on business need, for example, by restricting access based on IP address, granting temporary access privileges, or similar procedures.
The Personal Data Notification and Protection Act fills a void at the national level; most states have privacy laws with California pioneering the movement with SB 1386. However, enforcement at the state AG level has been uneven at best and absent at worse.
In preparing for this national legislation organization need to heed the policies derived from the FTC’s enforcement practices. They can also track the progress of this legislation and look for agencies such as the National Institute of Standards and Technology to issue guidance. Furthermore, organizations can encourage employees to take advantage of cybersecurity internship programs at nearby colleges and universities to avoid critical skills shortages.
With online security a clear priority for President Obama’s administration, it’s essential for organizations and consumers to understand upcoming legislation and learn the benefits/risks of sharing data. We’re looking forward to celebrating safeguarding data and enabling trust on Data Privacy Day, held annually on January 28, and hope that these tips will make 2015 your safest year yet.
As we head into Strata + Hadoop World San Jose, Pivotal has made some interesting announcements that are sure to be the talk of the show. Pivotal’s move to open-source some of their advanced products (and to form a new organization to foster Hadoop community cooperation) are signs of the dynamism and momentum of the Big Data market.
Informatica applauds these initiatives by Pivotal and we hope that they will contribute to the accelerating maturity of Hadoop and its expansion beyond early adopters into mainstream industry adoption. By contributing HAWQ, GemFire and the Greenplum Database to the open source community, Pivotal creates further open options in the evolving Hadoop data infrastructure technology. We expect this to be well received by the open source community.
As Informatica has long served as the industry’s neutral data connector for more than 5,500 customers and have developed a rich set of capabilities for Hadoop, we are also excited to see efforts to try to reduce fragmentation in the Hadoop community.
Even before the new company Pivotal was formed, Informatica had a long history working with the Greenplum team to ensure that joint customers could confidently use Informatica tools to include the Greenplum Database in their enterprise data pipelines. Informatica has mature and high-performance native connectivity to load data in and out of Greenplum reliably using Informatica’s codeless, visual data pipelining tools. In 2014, Informatica expanded out Hadoop support to include Pivotal HD Hadoop and we have joint customers using Informatica to do data profiling, transformation, parsing and cleansing using Informatica Big Data Edition running on Pivotal HD Hadoop.
We expect these innovative developments driven by Pivotal in the Big Data technology landscape to help to move the industry forward and contribute to Pivotal’s market progress. We look forward to continuing to support Pivotal technology and to an ever increasing number of successful joint customers. Please reach out to us if you have any questions about how Informatica and Pivotal can help your organization to put Big Data into production. We want to ensure that we can help you answer the question … Are you Big Data Ready?
First off, let me get one thing off my chest. If you don’t pay close attention to your data, throughout the application consolidation or migration process, you are almost guaranteed delays and budget overruns. Data consolidation and migration is at least 30%-40% of the application go-live effort. We have learned this by helping customers deliver over 1500 projects of this type. What’s worse, if you are not super meticulous about your data, you can be assured to encounter unhappy business stakeholders at the end of this treacherous journey. The users of your new application expect all their business-critical data to be there at the end of the road. All the bells and whistles in your new application will matter naught if the data falls apart. Imagine if you will, students’ transcripts gone missing, or your frequent-flyer balance a 100,000 miles short! Need I say more? Now, you may already be guessing where I am going with this. That’s right, we are talking about the myths and realities related to your data! Let’s explore a few of these.
Myth #1: All my data is there.
Reality #1: It may be there… But can you get it? if you want to find, access and move out all the data from your legacy systems, you must have a good set of connectivity tools to easily and automatically find, access and extract the data from your source systems. You don’t want to hand-code this for each source. Ouch!
Myth #2: I can just move my data from point A to point B.
Reality #2: You can try that approach if you want. However you might not be happy with the results. Reality is that there can be significant gaps and format mismatches between the data in your legacy system and the data required by your new application. Additionally you will likely need to assemble data from disparate systems. You need sophisticated tools to profile, assemble and transform your legacy data so that it is purpose-fit for your new application.
Myth #3: All my data is clean.
Reality #3: It’s not. And here is a tip: better profile, scrub and cleanse your data before you migrate it. You don’t want to put a shiny new application on top of questionable data . In other words let’s get a fresh start on the data in your new application!
Myth #4: All my data will move over as expected
Reality #4: It will not. Any time you move and transform large sets of data, there is room for logical or operational errors and surprises. The best way to avoid this is to automatically validate that your data has moved over as intended.
Myth #5: It’s a one-time effort.
Reality #5: ‘Load and explode’ is formula for disaster. Our proven methodology recommends you first prototype your migration path and identify a small subset of the data to move over. Then test it, tweak your model, try it again and gradually expand. More importantly, your application architecture should not be a one-time effort. It is work in progress and really an ongoing journey. Regardless of where you are on this journey, we recommend paying close attention to managing your application’s data foundation.
As you can see, there is a multitude of data issues that can plague an application consolidation or migration project and lead to its doom. These potential challenges are not always recognized and understood early on. This perception gap is a root-cause of project failure. This is why we are excited to host Philip Russom, of TDWI, in our upcoming webinar to discuss data management best practices and methodologies for application consolidation and migration. If you are undertaking any IT modernization or rationalization project, such as consolidating applications or migrating legacy applications to the cloud or to ‘on-prem’ application, such as SAP, this webinar is a must-see.
So what’s your reality going to be like? Will your project run like a dream or will it escalate into a scary nightmare? Here’s hoping for the former. And also hoping you can join us for this upcoming webinar to learn more:
Webinar with TDWI:
Successful Application Consolidation & Migration: Data Management Best Practices.
Date: Tuesday March 10, 10 am PT / 1 pm ET
Don’t miss out, Register Today!
1) Gartner report titled “Best Practices Mitigate Data Migration Risks and Challenges” published on December 9, 2014
2) Harvard Business Review: ‘Why your IT project may be riskier than you think’.
A lot of the trends we are seeing in enterprise integration today are being driven by the adoption of cloud based technologies from IaaS, PaaS and SaaS. I just was reading this story about a recent survey on cloud adoption and thought that a lot of this sounds very similar to things that we have seen before in enterprise IT.
Why discuss this? What can we learn? A couple of competing quotes come to mind.
Those who forget the past are bound to repeat it. – Edmund Burke
We are doomed to repeat the past no matter what. – Kurt Vonnegut
While every enterprise has to deal with their own complexities there are several past technology adoption patterns that can be used to drive discussion and compare today’s issues in order to drive decisions in how a company designs and deploys their current enterprise cloud architecture. Flexibility in design should be a key goal in addition to satisfying current business and technical requirements. So, what are the big patterns we have seen in the last 25 years that have shaped the cloud integration discussion?
1. 90s: Migration and replacement at the solution or application level. A big trend of the 90s was replacing older home grown systems or main frame based solutions with new packaged software solutions. SAP really started a lot of this with ERP and then we saw the rise of additional solutions for CRM, SCM, HRM, etc.
This kept a lot of people that do data integration very busy. From my point of view this era was very focused on replacement of technologies and this drove a lot of focus on data migration. While there were some scenarios around data integration to leave solutions in place these tended to be more in the area of systems that required transactional integrity and high level of messaging or back office solutions. On the classic front office solutions enterprises in large numbers did rip & replace and migration to new solutions.
2. 00s: Embrace and extend existing solutions with web applications. The rise of the Internet Browser combined with a popular and powerful standard programming language in Java shaped and drove enterprise integration in this time period. In addition, due to many of the mistakes and issues that IT groups had in the 90s there appeared to be a very strong drive to extend existing investments and not do rip and replace. IT and businesses were trying to figure out how to add new solutions to what they had in place. A lot of enterprise integration, service bus and what we consider as classic application development and deployment solutions came to market and were put in place.
3. 00s: Adoption of new web application based packaged solutions. A big part of this trend was driven by .Net & Java becoming more or less the de-facto desired language of enterprise IT. Software vendors not on these platforms were for the most part forced to re-platform or lose customers. New software vendors in many ways had an advantage because enterprises were already looking at large data migration to upgrade the solutions they had in place. In either case IT shops were looking to be either a .Net or Java shop and it caused a lot of churn.
4. 00s: First generation cloud applications and platforms. The first adoption of cloud applications and platforms were driven by projects and specific company needs. From Salesforce.com being used just for sales management before it became a platform to Amazon being used as just a run-time to develop and deploy applications before it became a full scale platform and an every growing list of examples as every vendor wants to be the cloud platform of choice. The integration needs originally were often on the light side because so many enterprises treated it as an experiment at first or a one off for a specific set of users. This has changed a lot in the last 10 years as many companies repeated their on premise silo of data problems in the cloud as they usage went from one cloud app to 2, 5, +10, etc. In fact, if you strip away where a solution happens to be deployed (on prem or cloud) the reality is that if an enterprise had previously had a poorly planned on premise architecture and solution portfolio they probably have just as poorly planned cloud architecture solution and portfolio. Adding them together just leads to disjoint solutions that are hard to integrate, hard to maintain and hard to evolve. In other words the opposite of the being flexible goal.
5. 10s: Consolidation of technology and battle of the cloud platforms. It appears we are just getting started in the next great market consolidation and every enterprise IT group is going to need to decide their own criteria for how they balance current and future investments. Today we have Salesforce, Amazon, Google, Apple, SAP and a few others. In 10 years some of these will either not exist as they do today or be marginalized. No one can say which ones for sure and this is why prioritizing flexibility in terms or architecture for cloud adoption.
For me the main take aways from the past 25 years of technology adoption trends for anyone that thinks about enterprise and data integration would be the following.
a) It’s all starts and ends with data. Yes, applications, process, and people are important but it’s about the data.
b) Coarse grain and loosely coupled approaches to integration are the most flexible. (e.g. avoid point to point at all costs)
c) Design with the knowledge of what data is critical and what data might or should be accessible or movable
d) Identify data and applications that might have to stay where it is no matter what.(e.g. the main frame is never dying)
e) Make sure your integration and application groups have access to or include someone that understand security. While a lot of integration developers think they understand security it’s usually after the fact that you find out they really do not.
So, it’s possible to shape your cloud adoption and architecture future by at least understanding how past technology and solution adoption has shaped the present. For me it is important to remember it is all about the data and prioritizing flexibility as a technology requirement at least at the same level as features and functions. Good luck.
As reviewed by Loraine Lawson, a MeriTalk survey about cloud adoption found that a “In the latest survey of 150 federal executives, nearly one in five say one-quarter of their IT services are fully or partially delivered via the cloud.”
For the most part, the shifts are more tactical in nature. These federal managers are shifting email (50 percent), web hosting (45 percent) and servers/storage (43 percent). Most interesting is that they’re not moving traditional business applications, custom business apps, or middleware. Why? Data, and data integration issues.
“Federal agencies are worried about what happens to data in the cloud, assuming they can get it there in the first place:
- 58 percent of executives fret about cloud-to-legacy system integration as a barrier.
- 57 percent are worried about migration challenges, suggesting they’re not sure the data can be moved at all.
- 54 percent are concerned about data portability once the data is in the cloud.
- 53 percent are worried about ‘contract lock-in.’ ”
The reality is that the government does not get much out of the movement to cloud without committing core business applications and thus core data. While e-mail and Web hosting, and some storage is good, the real cloud computing money is made when moving away from expensive hardware and software. Failing to do that, you fail to find the value, and, in this case, spend more taxpayer dollars than you should.
Data issues are not just a concern in the government. Most larger enterprise have the same issues as well. However, a few are able to get around these issues with good planning approaches and the right data management and data integration technology. It’s just a matter of making the initial leap, which most Federal IT executives are unwilling to do.
In working with CIOs of Federal agencies in the last few years, the larger issue is that of funding. While everyone understands that moving to cloud-based systems will save money, getting there means hiring government integrators and living with redundant systems for a time. That involves some major money. If most of the existing budget goes to existing IP operations, then the move may not be practical. Thus, there should be funds made available to work on the cloud projects with the greatest potential to reduce spending and increase efficiencies.
The shame of this situation is that the government was pretty much on the leading edge with cloud computing. back in 2008 and 2009. The CIO of the US Government, Vivek Kundra, promoted the use of cloud computing, and NIST drove the initial definitions of “The Cloud,” including IaaS, SaaS, and PaaS. But, when it came down to making the leap, most agencies balked at the opportunity citing issues with data.
Now that the technology has evolved even more, there is really no excuse for the government to delay migration to cloud-based platforms. The clouds are ready, and the data integration tools have cloud integration capabilities backed in. It’s time to see some more progress.
This blog post initially appeared on CMSwire.com and is reblogged here with their consent.
Friends of mine were remodeling their master bath. After searching for a claw foot tub in stores and online, they found the perfect one that fit their space. It was only available for purchase on the retailer’s e-commerce site, they bought it online.
When it arrived, the tub was too big. The dimensions online were incorrect. They went to return it to the closest store, but were told they couldn’t — because it was purchased online, they had to ship it back.
The retailer didn’t have a total customer relationship view or a single view of product information or inventory across channels and touch points. This left the customer representative working with a system that was a silo of limited information. She didn’t have access to a rich customer profile. She didn’t know that Joe and his wife spent almost $10,000 with the brand in the last year. She couldn’t see the products they bought online and in stores. Without this information, she couldn’t deliver a great customer experience.
It was a terrible customer experience. My friends share it with everyone who asks about their remodel. They name the retailer when they tell the story. And, they don’t shop there anymore. This terrible customer experience is negatively impacting the retailer’s revenue and brand reputation.
Bad customer experiences happen a lot. Companies in the US lose an estimated $83 billion each year due to defections and abandoned purchases as a direct result of a poor experience, according to a Datamonitor/Ovum report.
Customer Experience is the New Marketing
Gartner believes that by 2016, companies will compete primarily on the customer experiences they deliver. So who should own customer experience?
Twenty-five percent of CMOs say that their CEOs expect them to lead customer experience. What’s their definition of customer experience? “The practice of centralizing customer data in an effort to provide customers with the best possible interactions with every part of the company, from marketing to sales and even finance.”
Mercedes Benz USA President and CEO, Steve Cannon said, “Customer experience is the new marketing.”
The Gap Between Customer Expectations + Your Ability to Deliver
My previous post, 3 Barriers to Delivering Omnichannel Experiences, explained how omnichannel is all about seeing your business through the eyes of your customer. Customers don’t think in terms of channels and touch points, they just expect a seamless, integrated and consistent customer experience. It’s one brand to the customer. But there’s a gap between customer expectations and what most businesses can deliver today.
Most companies who sell through multiple channels operate in silos. They are channel-centric rather than customer-centric. This business model doesn’t empower employees to deliver seamless, integrated and consistent customer experiences across channels and touch points. Different leaders manage each channel and are held accountable to their own P&L. In most cases, there’s no incentive for leaders to collaborate.
Old Navy’s CMO, Ivan Wicksteed got it right when he said,
“Seventy percent of searches for Old Navy are on a mobile device. Consumers look at the product online and often want to touch it in the store. The end goal is not to get them to buy in the store. The end goal is to get them to buy.”
The end goal is what incentives should be based on.
Executives at most organizations I’ve spoken with admit they are at the very beginning stages of their journey to becoming omnichannel retailers. They recognize that empowering employees with a total customer relationship view and a single view of product information and inventory across channels are critical success factors.
Becoming an omnichannel business is not an easy transition. It forces executives to rethink their definition of customer-centricity and whether their business model supports it. “Now that we need to deliver seamless, integrated and consistent customer experiences across channels and touch points, we realized we’re not as customer-centric as we thought we were,” admitted an SVP of marketing at a financial services company.
You Have to Transform Your Business
“We’re going through a transformation to empower our employees to deliver great customer experiences at every stage of the customer journey,” said Chris Brogan, SVP of Strategy and Analytics at Hyatt Hotels & Resorts. “Our competitive differentiation comes from knowing our customers better than our competitors. We manage our customer data like a strategic asset so we can use that information to serve customers better and build loyalty for our brand.”
Hyatt uses data integration, data quality and master data management (MDM) technology to connect the numerous applications that contain fragmented customer data including sales, marketing, e-commerce, customer service and finance. It brings the core customer profiles together into a single, trusted location, where they are continually managed. Now its customer profiles are clean, de-duplicated, enriched and validated. Members of a household as well as the connections between corporate hierarchies are now visible. Business and analytics applications are fueled with this clean, consistent and connected information so customer-facing teams can do their jobs more effectively.
When he first joined Hyatt, Brogan did a search for his name in the central customer database and found 13 different versions of himself. This included the single Chris Brogan who lived across the street from Wrigley Field with his buddies in his 20s and the Chris Brogan who lives in the suburbs with his wife and two children. “I can guarantee those two guys want something very different from a hotel stay,” he joked. Those guest profiles have now been successfully consolidated.
According to Brogan,
“Successful marketing, sales and customer experience initiatives need to be built on a solid customer data foundation. It’s much harder to execute effectively and continually improve if your customer data is a mess.”
Improving How You Manage, Use and Analyze Data is More Important Than Ever
Some companies lack a single view of product information across channels and touch points. About 60 percent of retail managers believe that shoppers are better connected to product information than in-store associates. That’s a problem. The same challenges exist for product information as customer information. How many different systems contain valuable product information?
Harrods overcame this challenge. The retailer has a strategic initiative to transform from a single iconic store to an omnichannel business. In the past, Harrods’ merchants managed information for about 500,000 products for the store point of sale system and a few catalogs. Now they are using product information management technology (PIM) to effectively manage and merchandise 1.7 million products in the store and online.
Because they are managing product information centrally, they can fuel the ERP system and e-commerce platform with full, searchable multimedia product information. Harrods has also reduced the time it takes to introduce new products and generate revenue from them. In less than one hour, buyers complete the process from sourcing to market readiness.
It Ends with Satisfied Customers
By 2016, you will need to be ready to compete primarily on the customer experiences you deliver across channels and touch points. This means really knowing who your customers are so you can serve them better. Many businesses will transform from a channel-centric business model to a truly customer-centric business model. They will no longer tolerate messy data. They will recognize the importance of arming marketing, sales, e-commerce and customer service teams with the clean, consistent and connected customer, product and inventory information they need to deliver seamless, integrated and consistent experiences across touch points. And all of us will be more satisfied customers.
The verdict is in. Data is now broadly perceived as a source of competitive advantage. We all feel the heat to deliver good data. It is no wonder organizations view Analytics initiatives as highly strategic. But the big question is, can you really trust your data? Or are you just creating pretty visualizations on top of bad data?
We also know there is a shift towards self-service Analytics. But did you know that according to Gartner, “through 2016, less than 10% of self-service BI initiatives will be governed sufficiently to prevent inconsistencies that adversely affect the business”?1 This means that you may actually show up at your next big meeting and have data that contradicts your colleague’s data. Perhaps you are not working off of the same version of the truth. Maybe you have siloed data on different systems and they are not working in concert? Or is your definition of ‘revenue’ or ‘leads’ different from that of your colleague’s?
So are we taking our data for granted? Are we just assuming that it’s all available, clean, complete, integrated and consistent? As we work with organizations to support their Analytics journey, we often find that the harsh realities of data are quite different from perceptions. Let’s further investigate this perception gap.
For one, people may assume they can easily access all data. In reality, if data connectivity is not managed effectively, we often need to beg borrow and steal to get the right data from the right person. If we are lucky. In less fortunate scenarios, we may need to settle for partial data or a cheap substitute for the data we really wanted. And you know what they say, the only thing worse than no data is bad data. Right?
Another common misperception is: “Our data is clean. We have no data quality issues”. Wrong again. When we work with organizations to profile their data, they are often quite surprised to learn that their data is full of errors and gaps. One company recently discovered within one minute of starting their data profiling exercise, that millions of their customer records contained the company’s own address instead of the customers’ addresses… Oops.
Another myth is that all data is integrated. In reality, your data may reside in multiple locations: in the cloud, on premise, in Hadoop and on mainframe and anything in between. Integrating data from all these disparate and heterogeneous data sources is not a trivial task, unless you have the right tools.
And here is one more consideration to mull over. Do you find yourself manually hunting down and combining data to reproduce the same ad hoc report over and over again? Perhaps you often find yourself doing this in the wee hours of the night? Why reinvent the wheel? It would be more productive to automate the process of data ingestion and integration for reusable and shareable reports and Analytics.
Simply put, you need great data for great Analytics. We are excited to host Philip Russom of TDWI in a webinar to discuss how data management best practices can enable successful Analytics initiatives.
And how about you? Can you trust your data? Please join us for this webinar to learn more about building a trust-relationship with your data!
- Gartner Report, ‘Predicts 2015: Power Shift in Business Intelligence and Analytics Will Fuel Disruption’; Authors: Josh Parenteau, Neil Chandler, Rita L. Sallam, Douglas Laney, Alan D. Duncan; Nov 21 2014
Back in 2004, we saw the rapid growth of SaaS providers such as Salesforce.com. However, there was typically no consistent data integration strategy to go along with the use of SaaS. In many instances, SaaS-delivered applications became the new data silos in the enterprise, silos that lacked a sound integration plan and integration technology.
10 years later, we’ve gotten to a point where we have the ability to solve problems using SaaS and data integration problems around the use of SaaS. However, we typically lack the knowledge and understanding of how to effectively use data integration technology within an enterprise to integrate SaaS problem domains.
Lawson looks at both sides of the SaaS integration argument. “Surveys certainly show that integration is less of a concern for SaaS than in the early days, when nearly 88 percent of SaaS companies said integration concerns would slow down adoption and more than 88 percent said it’s an important or extremely important factor in winning new customers.”
Again, while we’ve certainly gotten better at integration, we’re nowhere near being out of the woods. “A Dimensional Research survey of 350 IT executives showed that 67 percent cited data integration problems as a challenge with SaaS business applications. And as with traditional systems, integration can add hidden costs to your project if you ignore it.”
As I’ve stated many times in this blog, integration requires a bit of planning and the use of solid technology. While this does require some extra effort and money, the return on the value of this work is huge.
SaaS integration requires that you take a bit of a different approach than traditional enterprise integration. SaaS systems typically place your data behind well-defined APIs that can be accessed directly or through a data integration technology. While the information can be consumed by anything that can invoke an API, enterprises still have to deal with structure and content differences, and that’s typically best handled using the right data integration technology.
Other things to consider, things that are again often overlooked, is the need for both data governance and data security around your SaaS integration solution. There should be a centralized control mechanism to support the proper management and security of the data, as well as a mechanism to deal with data quality issues that often emerge when consuming data from any cloud computing services.
The reality is that SaaS is here to stay. Even enterprise software players that put off the move to SaaS-delivered systems, are not standing up SaaS offerings. The economics around the use of SaaS are just way to compelling. However, as SaaS-delivered systems become more common place, so will the emergence of new silos. This will not be an issue, if you leverage the right SaaS integration approach and technology. What will your approach be?